renée berry 🌿

Amazing outcome. Cursor executed the applied AI strategy flawlessly. Most people completely underestimated the market size in AI coding. The biggest developer tool exits in history were on the order of low billions of dollars. And it was already assumed to be a heavily competitive and somewhat saturated space when they really got going. Multiple mental models shattered near instantly. The market sizes for agentic coding was far larger than most realized. There was actually tons and tons of room to innovate between the user and the underlying model, and even on the model itself. Cursor provided out the applied AI playbook by figuring out the right product shape for agentic coding work, acting as a neutral layer between models and the workflows, post training models for the areas that make most sense to drive down costs or improve performance, building out the right infra stack for the relevant workflows, building out a strong category-aligned GTM motion, and more. Great set of lessons for anyone doing applied AI right now. We’re going to see a lot more of these.

Had meetings and a dinner with 20+ enterprise AI and IT leaders today. Lots of interesting conversations around the state of AI in large enterprises, especially regulated businesses. Here are some of general trends: * Agents are clearly the big thing. Enterprises moving from talking about chatbots to agents, though we’re still very early. Coding is still the dominant agentic use-case being adopted thus far, with other categories of across knowledge work starting to emerge. Lots of agentic work moving from pilots and PoCs into production, and some enterprises had lots of active live use-cases. * Agentic use-cases span every part of a business, from back office operations to client facing experiences from sales to customer onboarding workflows. General feeling is that agentic workflows will hit every part of an organization, often with biggest focus on delivering better for customers, getting better insights and intelligence from data and documents, speeding up high ROI workflows with agents, and so on. Very limited discussion on pure cost cutting. * Data and AI governance still remain core challenges. Getting data and content into a spot that agents can securely and easily operate on remains a huge task for more organizations. Years of data management fragmentation that wasn’t a problem now is an issue for enterprises looking to adopt agents. And governing what agents can do with data in a workflow still a major topic. * Identity emerging as a big topic. Can the agent have access to everything you have? In a world of dozens of agents working on behalf, potentially too much data exposure and scope for the agents. How do we manage agents with partitioned level of access to your information? * Lots of emerging questions on how we will budget for tokens across use-cases and teams. Companies don’t want to constrain use-cases, but equally need to be mindful of ultimate token budgets. This is going to become a bigger part of OpEx over time, and probably won’t make sense to be considered an IT budget anymore. Likely needs to be factored into the rest of operating expenses. * Interoperability is key. Every enterprise is deploying multiple AI systems right now, and it’s unlikely that there’s going to be a single platform to rule them all. Customers are getting savvier on how to handle agent interoperability, and this will be one of the biggest drivers of an AI stack going forward. Lots more takeaways than just this, but needless to say the momentum is building but equally enterprises are acutely aware of the change management and work ahead. Lots of opportunity right now.

The majority of American adults interact with AI several times a week. AI has become a mainstream technology faster than any other tool in history. What’s amazing is that we’re only in the earliest stages of what’s actually possible. ht @emollick